Overview

Brought to you by YData

Dataset statistics

 Profiling report - TrainProfiling report - Test
Number of variables1010
Number of observations695404225141
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory58.4 MiB18.9 MiB
Average record size in memory88.0 B88.0 B

Variable types

 Profiling report - TrainProfiling report - Test
Numeric1010

Alerts

Profiling report - TrainProfiling report - Test
Depth (m) is highly overall correlated with Qtn (-) and 3 other fieldsDepth (m) is highly overall correlated with u0 (kPa) and 2 other fieldsHigh correlation
Fr (%) is highly overall correlated with Qtn (-) and 2 other fieldsFr (%) is highly overall correlated with Qtn (-) and 2 other fieldsHigh correlation
Qtn (-) is highly overall correlated with Depth (m) and 6 other fieldsQtn (-) is highly overall correlated with Fr (%) and 3 other fieldsHigh correlation
Rf (%) is highly overall correlated with Fr (%) and 1 other fieldsRf (%) is highly overall correlated with Fr (%) and 1 other fieldsHigh correlation
fs (kPa) is highly overall correlated with Qtn (-) and 1 other fieldsfs (kPa) is highly overall correlated with Qtn (-) and 1 other fieldsHigh correlation
qc (MPa) is highly overall correlated with Fr (%) and 3 other fieldsqc (MPa) is highly overall correlated with Fr (%) and 3 other fieldsHigh correlation
u0 (kPa) is highly overall correlated with Depth (m) and 3 other fieldsu0 (kPa) is highly overall correlated with Depth (m) and 2 other fieldsHigh correlation
σ',v (kPa) is highly overall correlated with Depth (m) and 3 other fieldsσ',v (kPa) is highly overall correlated with Depth (m) and 3 other fieldsHigh correlation
σ,v (kPa) is highly overall correlated with Depth (m) and 3 other fieldsσ,v (kPa) is highly overall correlated with Depth (m) and 2 other fieldsHigh correlation
Oberhollenzer_classes has 37931 (5.5%) zeros Oberhollenzer_classes has 16066 (7.1%) zeros Zeros

Reproduction

 Profiling report - TrainProfiling report - Test
Analysis started2024-11-12 02:54:05.5205972024-11-12 02:55:40.212352
Analysis finished2024-11-12 02:55:40.1839352024-11-12 02:56:37.003358
Duration1 minute and 34.66 seconds56.79 seconds
Software versionydata-profiling vv4.12.0ydata-profiling vv4.12.0
Download configurationconfig.jsonconfig.json

Variables

Depth (m)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct75386439
Distinct (%)1.1%2.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean14.16653413.485577
 Profiling report - TrainProfiling report - Test
Minimum0.010.02
Maximum75.8565.19
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:38.174608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.010.02
5-th percentile1.561.39
Q16.365.97
median11.9811.68
Q318.6818.49
95-th percentile36.4231.86
Maximum75.8565.19
Range75.8465.17
Interquartile range (IQR)12.3212.52

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation10.7491419.9924045
Coefficient of variation (CV)0.758769990.74096971
Kurtosis2.80181242.6303474
Mean14.16653413.485577
Median Absolute Deviation (MAD)6.086.17
Skewness1.43587451.3261443
Sum9851464.73036156.4
Variance115.5440499.848147
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:39.012767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.16 325
 
< 0.1%
5.19 325
 
< 0.1%
5.15 325
 
< 0.1%
5.18 325
 
< 0.1%
5.17 324
 
< 0.1%
5.14 324
 
< 0.1%
7.03 324
 
< 0.1%
7.84 324
 
< 0.1%
5.04 323
 
< 0.1%
7.04 323
 
< 0.1%
Other values (7528) 692162
99.5%
ValueCountFrequency (%)
4.51 108
 
< 0.1%
4.53 107
 
< 0.1%
6.13 107
 
< 0.1%
6.01 107
 
< 0.1%
6.06 106
 
< 0.1%
6.1 106
 
< 0.1%
6.12 106
 
< 0.1%
4.52 106
 
< 0.1%
4.5 106
 
< 0.1%
4.09 105
 
< 0.1%
Other values (6429) 224077
99.5%
ValueCountFrequency (%)
0.01 8
 
< 0.1%
0.02 15
 
< 0.1%
0.03 26
 
< 0.1%
0.04 57
 
< 0.1%
0.05 118
< 0.1%
0.06 194
< 0.1%
0.07 201
< 0.1%
0.08 209
< 0.1%
0.09 211
< 0.1%
0.1 210
< 0.1%
ValueCountFrequency (%)
0.02 2
 
< 0.1%
0.03 8
 
< 0.1%
0.04 11
 
< 0.1%
0.05 40
< 0.1%
0.06 68
< 0.1%
0.07 77
< 0.1%
0.08 75
< 0.1%
0.09 76
< 0.1%
0.1 76
< 0.1%
0.11 75
< 0.1%
ValueCountFrequency (%)
0.02 2
 
< 0.1%
0.03 8
 
< 0.1%
0.04 11
 
< 0.1%
0.05 40
< 0.1%
0.06 68
< 0.1%
0.07 77
< 0.1%
0.08 75
< 0.1%
0.09 76
< 0.1%
0.1 76
< 0.1%
0.11 75
< 0.1%
ValueCountFrequency (%)
0.01 8
 
< 0.1%
0.02 15
 
< 0.1%
0.03 26
 
< 0.1%
0.04 57
 
< 0.1%
0.05 118
0.1%
0.06 194
0.1%
0.07 201
0.1%
0.08 209
0.1%
0.09 211
0.1%
0.1 210
0.1%

qc (MPa)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct68005640
Distinct (%)1.0%2.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.3614235.5948569
 Profiling report - TrainProfiling report - Test
Minimum0.010.01
Maximum92.6786.67
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:39.877754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.010.01
5-th percentile0.480.46
Q11.070.97
median2.522.57
Q36.016.56
95-th percentile20.6921.69
Maximum92.6786.67
Range92.6686.66
Interquartile range (IQR)4.945.59

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation8.20544468.2836627
Coefficient of variation (CV)1.53046021.4805853
Kurtosis16.86595914.745761
Mean5.3614235.5948569
Median Absolute Deviation (MAD)1.741.86
Skewness3.67497763.3630745
Sum37283551259631.7
Variance67.32932268.619067
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:40.661539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92 3217
 
0.5%
0.94 3070
 
0.4%
0.93 2907
 
0.4%
0.95 2881
 
0.4%
0.97 2877
 
0.4%
0.91 2792
 
0.4%
0.98 2769
 
0.4%
0.96 2759
 
0.4%
1 2747
 
0.4%
0.9 2726
 
0.4%
Other values (6790) 666659
95.9%
ValueCountFrequency (%)
0.9 1133
 
0.5%
0.84 1127
 
0.5%
0.91 1122
 
0.5%
0.85 1083
 
0.5%
0.8 1068
 
0.5%
0.89 1057
 
0.5%
0.81 1046
 
0.5%
0.83 1040
 
0.5%
0.82 1038
 
0.5%
0.78 1033
 
0.5%
Other values (5630) 214394
95.2%
ValueCountFrequency (%)
0.01 46
< 0.1%
0.02 86
< 0.1%
0.03 63
< 0.1%
0.04 50
< 0.1%
0.05 76
< 0.1%
0.06 85
< 0.1%
0.07 109
< 0.1%
0.08 95
< 0.1%
0.09 98
< 0.1%
0.1 80
< 0.1%
ValueCountFrequency (%)
0.01 25
< 0.1%
0.02 44
< 0.1%
0.03 21
< 0.1%
0.04 32
< 0.1%
0.05 38
< 0.1%
0.06 14
 
< 0.1%
0.07 14
 
< 0.1%
0.08 9
 
< 0.1%
0.09 13
 
< 0.1%
0.1 16
 
< 0.1%
ValueCountFrequency (%)
0.01 25
< 0.1%
0.02 44
< 0.1%
0.03 21
< 0.1%
0.04 32
< 0.1%
0.05 38
< 0.1%
0.06 14
 
< 0.1%
0.07 14
 
< 0.1%
0.08 9
 
< 0.1%
0.09 13
 
< 0.1%
0.1 16
 
< 0.1%
ValueCountFrequency (%)
0.01 46
< 0.1%
0.02 86
< 0.1%
0.03 63
< 0.1%
0.04 50
< 0.1%
0.05 76
< 0.1%
0.06 85
< 0.1%
0.07 109
< 0.1%
0.08 95
< 0.1%
0.09 98
< 0.1%
0.1 80
< 0.1%

fs (kPa)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct87925124
Distinct (%)1.3%2.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean53.79324155.563215
 Profiling report - TrainProfiling report - Test
Minimum0.010.01
Maximum1160.71191.2
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:41.444245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.010.01
5-th percentile6.77.2
Q11717.5
median33.432.8
Q366.565.1
95-th percentile162.8179.9
Maximum1160.71191.2
Range1160.691191.19
Interquartile range (IQR)49.547.6

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation65.05473671.334796
Coefficient of variation (CV)1.20934781.2838493
Kurtosis26.55749424.455086
Mean53.79324155.563215
Median Absolute Deviation (MAD)20.219
Skewness4.05370864.0138273
Sum3740803512509558
Variance4232.11865088.6531
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:42.281539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.9 1676
 
0.2%
11 1652
 
0.2%
10.4 1651
 
0.2%
10.3 1626
 
0.2%
9.9 1614
 
0.2%
9.3 1608
 
0.2%
9.8 1602
 
0.2%
15.9 1584
 
0.2%
12.1 1581
 
0.2%
9.2 1576
 
0.2%
Other values (8782) 679234
97.7%
ValueCountFrequency (%)
15.9 602
 
0.3%
14.3 588
 
0.3%
13 584
 
0.3%
13.1 571
 
0.3%
13.7 562
 
0.2%
13.2 557
 
0.2%
13.8 537
 
0.2%
14.6 535
 
0.2%
16.7 534
 
0.2%
16.5 531
 
0.2%
Other values (5114) 219540
97.5%
ValueCountFrequency (%)
0.01 5
 
< 0.1%
0.02 38
 
< 0.1%
0.03 33
 
< 0.1%
0.04 22
 
< 0.1%
0.05 12
 
< 0.1%
0.06 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 109
< 0.1%
ValueCountFrequency (%)
0.01 5
 
< 0.1%
0.02 28
< 0.1%
0.03 14
 
< 0.1%
0.04 25
< 0.1%
0.05 14
 
< 0.1%
0.06 4
 
< 0.1%
0.1 28
< 0.1%
0.2 30
< 0.1%
0.3 35
< 0.1%
0.4 40
< 0.1%
ValueCountFrequency (%)
0.01 5
 
< 0.1%
0.02 28
< 0.1%
0.03 14
 
< 0.1%
0.04 25
< 0.1%
0.05 14
 
< 0.1%
0.06 4
 
< 0.1%
0.1 28
< 0.1%
0.2 30
< 0.1%
0.3 35
< 0.1%
0.4 40
< 0.1%
ValueCountFrequency (%)
0.01 5
 
< 0.1%
0.02 38
 
< 0.1%
0.03 33
 
< 0.1%
0.04 22
 
< 0.1%
0.05 12
 
< 0.1%
0.06 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 109
< 0.1%

Rf (%)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct989954
Distinct (%)0.1%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.75850541.7701536
 Profiling report - TrainProfiling report - Test
Minimum0.010.01
Maximum9.939.89
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:43.173982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.010.01
5-th percentile0.240.26
Q10.720.72
median1.341.38
Q32.312.31
95-th percentile4.824.88
Maximum9.939.89
Range9.929.88
Interquartile range (IQR)1.591.59

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation1.48116991.470613
Coefficient of variation (CV)0.842289120.83078271
Kurtosis3.67587133.7495117
Mean1.75850541.7701536
Median Absolute Deviation (MAD)0.740.77
Skewness1.74698861.7502985
Sum1222871.7398534.16
Variance2.19386442.1627027
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:44.059146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41 3239
 
0.5%
0.39 3216
 
0.5%
0.4 3208
 
0.5%
0.36 3164
 
0.5%
0.42 3099
 
0.4%
0.34 3096
 
0.4%
0.31 3072
 
0.4%
0.3 3072
 
0.4%
0.38 3054
 
0.4%
0.29 3032
 
0.4%
Other values (979) 664152
95.5%
ValueCountFrequency (%)
0.37 1208
 
0.5%
0.36 1163
 
0.5%
0.33 1142
 
0.5%
0.34 1123
 
0.5%
0.32 1122
 
0.5%
0.35 1121
 
0.5%
0.4 1116
 
0.5%
0.39 1079
 
0.5%
0.31 1078
 
0.5%
0.44 1063
 
0.5%
Other values (944) 213926
95.0%
ValueCountFrequency (%)
0.01 317
 
< 0.1%
0.02 188
 
< 0.1%
0.03 298
 
< 0.1%
0.04 379
 
0.1%
0.05 409
 
0.1%
0.06 590
0.1%
0.07 664
0.1%
0.08 751
0.1%
0.09 905
0.1%
0.1 1053
0.2%
ValueCountFrequency (%)
0.01 169
0.1%
0.02 77
 
< 0.1%
0.03 73
 
< 0.1%
0.04 74
 
< 0.1%
0.05 86
 
< 0.1%
0.06 99
 
< 0.1%
0.07 143
0.1%
0.08 207
0.1%
0.09 214
0.1%
0.1 282
0.1%
ValueCountFrequency (%)
0.01 169
< 0.1%
0.02 77
 
< 0.1%
0.03 73
 
< 0.1%
0.04 74
 
< 0.1%
0.05 86
 
< 0.1%
0.06 99
 
< 0.1%
0.07 143
< 0.1%
0.08 207
< 0.1%
0.09 214
< 0.1%
0.1 282
< 0.1%
ValueCountFrequency (%)
0.01 317
 
0.1%
0.02 188
 
0.1%
0.03 298
 
0.1%
0.04 379
 
0.2%
0.05 409
 
0.2%
0.06 590
0.3%
0.07 664
0.3%
0.08 751
0.3%
0.09 905
0.4%
0.1 1053
0.5%

σ,v (kPa)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct83336439
Distinct (%)1.2%2.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean269.16418256.22597
 Profiling report - TrainProfiling report - Test
Minimum0.190.38
Maximum1441.151238.61
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:44.976337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.190.38
5-th percentile29.6426.41
Q1120.84113.43
median227.62221.92
Q3354.92351.31
95-th percentile691.98605.34
Maximum1441.151238.61
Range1440.961238.23
Interquartile range (IQR)234.08237.88

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation204.23372189.85568
Coefficient of variation (CV)0.758770050.74096971
Kurtosis2.80180822.6303474
Mean269.16418256.22597
Median Absolute Deviation (MAD)115.52117.23
Skewness1.4358741.3261443
Sum1.8717785 × 10857686972
Variance41711.41336045.181
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:45.827775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.61 325
 
< 0.1%
98.04 325
 
< 0.1%
98.42 325
 
< 0.1%
97.85 325
 
< 0.1%
97.66 324
 
< 0.1%
98.23 324
 
< 0.1%
148.96 324
 
< 0.1%
133.57 324
 
< 0.1%
97.28 323
 
< 0.1%
101.46 323
 
< 0.1%
Other values (8323) 692162
99.5%
ValueCountFrequency (%)
85.69 108
 
< 0.1%
86.07 107
 
< 0.1%
116.47 107
 
< 0.1%
114.19 107
 
< 0.1%
115.14 106
 
< 0.1%
115.9 106
 
< 0.1%
116.28 106
 
< 0.1%
85.88 106
 
< 0.1%
85.5 106
 
< 0.1%
77.71 105
 
< 0.1%
Other values (6429) 224077
99.5%
ValueCountFrequency (%)
0.19 8
 
< 0.1%
0.38 15
 
< 0.1%
0.57 26
 
< 0.1%
0.76 57
 
< 0.1%
0.95 118
< 0.1%
1.14 194
< 0.1%
1.33 201
< 0.1%
1.52 209
< 0.1%
1.71 211
< 0.1%
1.9 210
< 0.1%
ValueCountFrequency (%)
0.38 2
 
< 0.1%
0.57 8
 
< 0.1%
0.76 11
 
< 0.1%
0.95 40
< 0.1%
1.14 68
< 0.1%
1.33 77
< 0.1%
1.52 75
< 0.1%
1.71 76
< 0.1%
1.9 76
< 0.1%
2.09 75
< 0.1%
ValueCountFrequency (%)
0.38 2
 
< 0.1%
0.57 8
 
< 0.1%
0.76 11
 
< 0.1%
0.95 40
< 0.1%
1.14 68
< 0.1%
1.33 77
< 0.1%
1.52 75
< 0.1%
1.71 76
< 0.1%
1.9 76
< 0.1%
2.09 75
< 0.1%
ValueCountFrequency (%)
0.19 8
 
< 0.1%
0.38 15
 
< 0.1%
0.57 26
 
< 0.1%
0.76 57
 
< 0.1%
0.95 118
0.1%
1.14 194
0.1%
1.33 201
0.1%
1.52 209
0.1%
1.71 211
0.1%
1.9 210
0.1%

u0 (kPa)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct83546440
Distinct (%)1.2%2.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean131.52354126.64863
 Profiling report - TrainProfiling report - Test
Minimum0.10.1
Maximum744.09639.51
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:46.739281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.10.1
5-th percentile11.6711.09
Q155.0352.88
median109.48108.2
Q3176.19175.5
95-th percentile346.78305.19
Maximum744.09639.51
Range743.99639.41
Interquartile range (IQR)121.16122.62

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation104.6436897.663357
Coefficient of variation (CV)0.795626960.77113629
Kurtosis3.08720982.9211755
Mean131.52354126.64863
Median Absolute Deviation (MAD)59.3560.24
Skewness1.49482171.3957386
Sum9146199828513800
Variance10950.2999538.1312
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:47.689897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.62 332
 
< 0.1%
50.52 332
 
< 0.1%
50.91 331
 
< 0.1%
50.82 331
 
< 0.1%
52.29 331
 
< 0.1%
50.23 331
 
< 0.1%
50.42 331
 
< 0.1%
50.72 331
 
< 0.1%
40.22 330
 
< 0.1%
49.34 330
 
< 0.1%
Other values (8344) 692094
99.5%
ValueCountFrequency (%)
40.91 111
 
< 0.1%
41.1 111
 
< 0.1%
41.01 111
 
< 0.1%
44.24 110
 
< 0.1%
39.93 110
 
< 0.1%
42.97 110
 
< 0.1%
43.07 110
 
< 0.1%
41.2 110
 
< 0.1%
15.3 110
 
< 0.1%
40.12 110
 
< 0.1%
Other values (6430) 224038
99.5%
ValueCountFrequency (%)
0.1 87
 
< 0.1%
0.2 94
 
< 0.1%
0.29 104
 
< 0.1%
0.39 135
< 0.1%
0.49 196
< 0.1%
0.59 271
< 0.1%
0.69 278
< 0.1%
0.78 288
< 0.1%
0.88 291
< 0.1%
0.98 290
< 0.1%
ValueCountFrequency (%)
0.1 21
 
< 0.1%
0.2 22
 
< 0.1%
0.29 28
 
< 0.1%
0.39 31
 
< 0.1%
0.49 60
< 0.1%
0.59 89
< 0.1%
0.69 98
< 0.1%
0.78 96
< 0.1%
0.88 98
< 0.1%
0.98 98
< 0.1%
ValueCountFrequency (%)
0.1 21
 
< 0.1%
0.2 22
 
< 0.1%
0.29 28
 
< 0.1%
0.39 31
 
< 0.1%
0.49 60
< 0.1%
0.59 89
< 0.1%
0.69 98
< 0.1%
0.78 96
< 0.1%
0.88 98
< 0.1%
0.98 98
< 0.1%
ValueCountFrequency (%)
0.1 87
 
< 0.1%
0.2 94
 
< 0.1%
0.29 104
 
< 0.1%
0.39 135
0.1%
0.49 196
0.1%
0.59 271
0.1%
0.69 278
0.1%
0.78 288
0.1%
0.88 291
0.1%
0.98 290
0.1%

σ',v (kPa)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct4184828173
Distinct (%)6.0%12.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean137.64073129.57743
 Profiling report - TrainProfiling report - Test
Minimum0.090.18
Maximum697.06599.1
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:48.536001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.090.18
5-th percentile14.5212.87
Q163.7859.18
median118.09113.77
Q3180.95177.19
95-th percentile345.91303.02
Maximum697.06599.1
Range696.97598.92
Interquartile range (IQR)117.17118.01

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation101.6791493.675889
Coefficient of variation (CV)0.738728590.72293368
Kurtosis2.31201182.1627607
Mean137.64073129.57743
Median Absolute Deviation (MAD)58.0858.37
Skewness1.32354131.216972
Sum9571591529173192
Variance10338.6488775.1721
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:49.502800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.7 250
 
< 0.1%
48.98 249
 
< 0.1%
46.23 249
 
< 0.1%
47.33 249
 
< 0.1%
47.42 249
 
< 0.1%
47.24 249
 
< 0.1%
46.69 248
 
< 0.1%
49.07 248
 
< 0.1%
47.05 248
 
< 0.1%
46.04 248
 
< 0.1%
Other values (41838) 692917
99.6%
ValueCountFrequency (%)
9.19 90
 
< 0.1%
38.51 89
 
< 0.1%
17.09 88
 
< 0.1%
14.34 88
 
< 0.1%
9.74 88
 
< 0.1%
29.78 88
 
< 0.1%
9.47 88
 
< 0.1%
9.65 88
 
< 0.1%
9.1 88
 
< 0.1%
14.43 88
 
< 0.1%
Other values (28163) 224258
99.6%
ValueCountFrequency (%)
0.09 8
 
< 0.1%
0.18 15
 
< 0.1%
0.28 26
 
< 0.1%
0.37 57
 
< 0.1%
0.46 118
< 0.1%
0.55 194
< 0.1%
0.64 201
< 0.1%
0.74 209
< 0.1%
0.83 211
< 0.1%
0.92 210
< 0.1%
ValueCountFrequency (%)
0.18 2
 
< 0.1%
0.28 8
 
< 0.1%
0.37 11
 
< 0.1%
0.46 40
< 0.1%
0.55 68
< 0.1%
0.64 77
< 0.1%
0.74 75
< 0.1%
0.83 76
< 0.1%
0.92 76
< 0.1%
1.01 75
< 0.1%
ValueCountFrequency (%)
0.18 2
 
< 0.1%
0.28 8
 
< 0.1%
0.37 11
 
< 0.1%
0.46 40
< 0.1%
0.55 68
< 0.1%
0.64 77
< 0.1%
0.74 75
< 0.1%
0.83 76
< 0.1%
0.92 76
< 0.1%
1.01 75
< 0.1%
ValueCountFrequency (%)
0.09 8
 
< 0.1%
0.18 15
 
< 0.1%
0.28 26
 
< 0.1%
0.37 57
 
< 0.1%
0.46 118
0.1%
0.55 194
0.1%
0.64 201
0.1%
0.74 209
0.1%
0.83 211
0.1%
0.92 210
0.1%

Qtn (-)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct5008831975
Distinct (%)7.2%14.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean60.4104363.263086
 Profiling report - TrainProfiling report - Test
Minimum0.180.22
Maximum10011001
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:50.352077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.180.22
5-th percentile1.981.89
Q15.535.62
median22.8725.97
Q358.4965.41
95-th percentile274.697280.29
Maximum10011001
Range1000.821000.78
Interquartile range (IQR)52.9659.79

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation111.6123110.53207
Coefficient of variation (CV)1.84756671.7471812
Kurtosis18.68396418.174503
Mean60.4104363.263086
Median Absolute Deviation (MAD)19.1822.4
Skewness3.8913783.7625557
Sum4200965414243114
Variance12457.30512217.339
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:51.183187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.08 567
 
0.1%
2.06 566
 
0.1%
2.03 565
 
0.1%
2.01 558
 
0.1%
1.38 555
 
0.1%
2.13 553
 
0.1%
2.16 551
 
0.1%
2.05 550
 
0.1%
2.04 547
 
0.1%
2.09 545
 
0.1%
Other values (50078) 689847
99.2%
ValueCountFrequency (%)
1.9 461
 
0.2%
1.91 455
 
0.2%
1.89 432
 
0.2%
1.92 428
 
0.2%
1.88 415
 
0.2%
1.93 403
 
0.2%
1.87 398
 
0.2%
1.97 389
 
0.2%
1.86 378
 
0.2%
1.94 371
 
0.2%
Other values (31965) 221011
98.2%
ValueCountFrequency (%)
0.18 1
< 0.1%
0.21 1
< 0.1%
0.25 1
< 0.1%
0.27 2
< 0.1%
0.29 1
< 0.1%
0.31 1
< 0.1%
0.34 2
< 0.1%
0.35 1
< 0.1%
0.37 1
< 0.1%
0.38 1
< 0.1%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.42 1
 
< 0.1%
0.49 1
 
< 0.1%
0.58 2
 
< 0.1%
0.59 6
< 0.1%
0.6 1
 
< 0.1%
0.61 3
< 0.1%
0.62 4
< 0.1%
0.63 5
< 0.1%
0.64 1
 
< 0.1%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.42 1
 
< 0.1%
0.49 1
 
< 0.1%
0.58 2
 
< 0.1%
0.59 6
< 0.1%
0.6 1
 
< 0.1%
0.61 3
< 0.1%
0.62 4
< 0.1%
0.63 5
< 0.1%
0.64 1
 
< 0.1%
ValueCountFrequency (%)
0.18 1
< 0.1%
0.21 1
< 0.1%
0.25 1
< 0.1%
0.27 2
< 0.1%
0.29 1
< 0.1%
0.31 1
< 0.1%
0.34 2
< 0.1%
0.35 1
< 0.1%
0.37 1
< 0.1%
0.38 1
< 0.1%

Fr (%)
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct999999
Distinct (%)0.1%0.4%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.25322272.2852119
 Profiling report - TrainProfiling report - Test
Minimum0.010.01
Maximum9.999.99
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:51.977690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum0.010.01
5-th percentile0.250.27
Q10.810.78
median1.671.68
Q33.113.26
95-th percentile6.416.3
Maximum9.999.99
Range9.989.98
Interquartile range (IQR)2.32.48

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation1.94025371.9476584
Coefficient of variation (CV)0.861101610.8522879
Kurtosis1.90881771.6490471
Mean2.25322272.2852119
Median Absolute Deviation (MAD)1.051.1
Skewness1.43025421.3453228
Sum1566900.1514494.89
Variance3.76458453.7933734
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:52.852939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.37 2995
 
0.4%
0.36 2968
 
0.4%
0.33 2919
 
0.4%
0.34 2836
 
0.4%
0.31 2836
 
0.4%
0.38 2829
 
0.4%
0.41 2828
 
0.4%
0.32 2827
 
0.4%
0.45 2807
 
0.4%
0.35 2781
 
0.4%
Other values (989) 666778
95.9%
ValueCountFrequency (%)
0.37 1210
 
0.5%
0.38 1111
 
0.5%
0.33 1096
 
0.5%
0.34 1093
 
0.5%
0.35 1078
 
0.5%
0.4 1066
 
0.5%
0.36 1065
 
0.5%
0.39 1050
 
0.5%
0.44 1033
 
0.5%
0.43 1031
 
0.5%
Other values (989) 214308
95.2%
ValueCountFrequency (%)
0.01 264
 
< 0.1%
0.02 189
 
< 0.1%
0.03 256
 
< 0.1%
0.04 322
 
< 0.1%
0.05 385
0.1%
0.06 551
0.1%
0.07 586
0.1%
0.08 732
0.1%
0.09 799
0.1%
0.1 925
0.1%
ValueCountFrequency (%)
0.01 159
0.1%
0.02 81
 
< 0.1%
0.03 70
 
< 0.1%
0.04 73
 
< 0.1%
0.05 77
 
< 0.1%
0.06 103
< 0.1%
0.07 119
0.1%
0.08 200
0.1%
0.09 212
0.1%
0.1 238
0.1%
ValueCountFrequency (%)
0.01 159
< 0.1%
0.02 81
 
< 0.1%
0.03 70
 
< 0.1%
0.04 73
 
< 0.1%
0.05 77
 
< 0.1%
0.06 103
< 0.1%
0.07 119
< 0.1%
0.08 200
< 0.1%
0.09 212
< 0.1%
0.1 238
< 0.1%
ValueCountFrequency (%)
0.01 264
 
0.1%
0.02 189
 
0.1%
0.03 256
 
0.1%
0.04 322
 
0.1%
0.05 385
0.2%
0.06 551
0.2%
0.07 586
0.3%
0.08 732
0.3%
0.09 799
0.4%
0.1 925
0.4%

Oberhollenzer_classes
Real number (ℝ)

 Profiling report - TrainProfiling report - Test
Distinct77
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean4.22838814.0664739
 Profiling report - TrainProfiling report - Test
Minimum00
Maximum77
Zeros3793116066
Zeros (%)5.5%7.1%
Negative00
Negative (%)0.0%0.0%
Memory size10.6 MiB3.4 MiB
2024-11-11T21:56:53.404164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

 Profiling report - TrainProfiling report - Test
Minimum00
5-th percentile00
Q122
median55
Q366
95-th percentile77
Maximum77
Range77
Interquartile range (IQR)44

Descriptive statistics

 Profiling report - TrainProfiling report - Test
Standard deviation2.202542.254646
Coefficient of variation (CV)0.520893540.55444744
Kurtosis-1.1538601-1.2101933
Mean4.22838814.0664739
Median Absolute Deviation (MAD)22
Skewness-0.41989714-0.35167781
Sum2940438915530
Variance4.85118265.0834288
MonotonicityNot monotonicNot monotonic
2024-11-11T21:56:53.894562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 155490
22.4%
2 135616
19.5%
6 127983
18.4%
7 116888
16.8%
4 61382
 
8.8%
1 60114
 
8.6%
0 37931
 
5.5%
ValueCountFrequency (%)
5 44611
19.8%
6 39103
17.4%
2 38391
17.1%
7 35190
15.6%
4 27655
12.3%
1 24125
10.7%
0 16066
 
7.1%
ValueCountFrequency (%)
0 37931
 
5.5%
1 60114
 
8.6%
2 135616
19.5%
4 61382
 
8.8%
5 155490
22.4%
6 127983
18.4%
7 116888
16.8%
ValueCountFrequency (%)
0 16066
 
7.1%
1 24125
10.7%
2 38391
17.1%
4 27655
12.3%
5 44611
19.8%
6 39103
17.4%
7 35190
15.6%
ValueCountFrequency (%)
0 16066
 
2.3%
1 24125
3.5%
2 38391
5.5%
4 27655
4.0%
5 44611
6.4%
6 39103
5.6%
7 35190
5.1%
ValueCountFrequency (%)
0 37931
 
16.8%
1 60114
 
26.7%
2 135616
60.2%
4 61382
 
27.3%
5 155490
69.1%
6 127983
56.8%
7 116888
51.9%

Interactions

Profiling report - Train

2024-11-11T21:55:20.621457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:28.511153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

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2024-11-11T21:55:49.709184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:30.819041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:54.704060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:36.153115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:59.278765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:40.392129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:03.445997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:44.395801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:08.010510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:51.206227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:12.195217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:58.943712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:16.431464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:06.809341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:20.737210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:14.060746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:25.061057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:25.159903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:30.258268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:26.606366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:50.185341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:31.334601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:55.187552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:36.548021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:59.668447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:40.789003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:03.888509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:44.739441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:08.400923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:52.190649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:12.607024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:59.586254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:16.813890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:07.556753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:21.134071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:14.708752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:25.437789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:26.527945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:30.813961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:27.033798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:50.631088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:31.749557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:55.635059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:36.905571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:00.157976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:41.145641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:04.262664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:45.186288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:08.796993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:52.867961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:13.025994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:00.404976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:17.186690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:08.160263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:21.509053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:15.364129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:25.818546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:27.979851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:31.795598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:27.527934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:51.154374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:32.183586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:56.042939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:37.380784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:00.535125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:41.486480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:04.689203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:45.827905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:09.225150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:53.564730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:13.439803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:01.080822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:17.611859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:08.850043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:21.865949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:16.024257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:26.251419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:29.422524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:32.381415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:27.929492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:51.609804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:32.709581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:56.545217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:37.808639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:00.919229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:41.899748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:05.478491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:46.526242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:09.593600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:54.242100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:13.901788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:01.740340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:17.992911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:09.505685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:22.313414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:16.716383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:26.623090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:30.769717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:32.919339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:28.353235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:52.050957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:33.198604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:56.963530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:38.147275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:01.314979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:42.254905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:05.908232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:47.190911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:09.977618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:54.958748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:14.312088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:02.438560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:18.391018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:10.152256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:22.736640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:17.373499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:27.028523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:32.237065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:33.471598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:28.680280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:52.521261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:33.675462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:57.411115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:38.528242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:01.715925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:42.565947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:06.284963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:47.865516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:10.399520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:55.574178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:14.700617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:03.176355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:18.843768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:10.733246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:23.182661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:17.996865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:27.398140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:34.224751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:34.174435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:29.528646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:53.213609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:34.534193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:55:58.111180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:39.267688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:02.253750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:43.288839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:06.882981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:49.202259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:11.027690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:54:56.916387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:15.291634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:04.528596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:19.524218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:12.107052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:23.737910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

2024-11-11T21:55:19.306133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:27.946996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

Profiling report - Train

2024-11-11T21:56:54.309480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Test

2024-11-11T21:56:54.929208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Profiling report - Train

Depth (m)Fr (%)Oberhollenzer_classesQtn (-)Rf (%)fs (kPa)qc (MPa)u0 (kPa)σ',v (kPa)σ,v (kPa)
Depth (m)1.0000.1050.210-0.562-0.023-0.052-0.0370.9820.9851.000
Fr (%)0.1051.0000.292-0.5950.9650.043-0.6750.1160.0920.105
Oberhollenzer_classes0.2100.2921.000-0.4520.223-0.266-0.4020.2020.2120.210
Qtn (-)-0.562-0.595-0.4521.000-0.4160.5660.813-0.554-0.552-0.562
Rf (%)-0.0230.9650.223-0.4161.0000.198-0.554-0.011-0.033-0.023
fs (kPa)-0.0520.043-0.2660.5660.1981.0000.663-0.061-0.043-0.052
qc (MPa)-0.037-0.675-0.4020.813-0.5540.6631.000-0.052-0.022-0.037
u0 (kPa)0.9820.1160.202-0.554-0.011-0.061-0.0521.0000.9360.982
σ',v (kPa)0.9850.0920.212-0.552-0.033-0.043-0.0220.9361.0000.985
σ,v (kPa)1.0000.1050.210-0.562-0.023-0.052-0.0370.9820.9851.000

Profiling report - Test

Depth (m)Fr (%)Oberhollenzer_classesQtn (-)Rf (%)fs (kPa)qc (MPa)u0 (kPa)σ',v (kPa)σ,v (kPa)
Depth (m)1.0000.0790.191-0.494-0.046-0.0160.0200.9880.9901.000
Fr (%)0.0791.0000.285-0.6450.966-0.056-0.7100.0790.0770.079
Oberhollenzer_classes0.1910.2851.000-0.4150.216-0.246-0.3480.1740.2020.191
Qtn (-)-0.494-0.645-0.4151.000-0.4790.6000.828-0.467-0.509-0.494
Rf (%)-0.0460.9660.216-0.4791.0000.094-0.602-0.040-0.051-0.046
fs (kPa)-0.016-0.056-0.2460.6000.0941.0000.6990.005-0.034-0.016
qc (MPa)0.020-0.710-0.3480.828-0.6020.6991.0000.0360.0050.020
u0 (kPa)0.9880.0790.174-0.467-0.0400.0050.0361.0000.9570.988
σ',v (kPa)0.9900.0770.202-0.509-0.051-0.0340.0050.9571.0000.990
σ,v (kPa)1.0000.0790.191-0.494-0.046-0.0160.0200.9880.9901.000

Missing values

Profiling report - Train

2024-11-11T21:55:34.925607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Profiling report - Test

2024-11-11T21:56:34.713647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.

Profiling report - Train

2024-11-11T21:55:36.767686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Profiling report - Test

2024-11-11T21:56:35.807831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Profiling report - Train

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
14885150.056.855.800.090.950.490.46263.950.094.0
14885160.067.9211.600.171.140.590.55315.950.174.0
14885170.079.4423.200.211.330.690.64339.460.214.0
14885180.0810.8423.200.231.520.780.74364.640.234.0
14885190.0911.2825.600.221.710.880.83369.590.224.0
14885200.1011.2826.200.241.900.980.92368.320.244.0
14885210.1110.7226.900.272.091.081.01377.870.274.0
14885220.1210.2132.900.322.281.181.10371.230.324.0
14885230.138.7435.600.392.471.281.19359.190.394.0
14885240.148.4838.300.442.661.371.29352.540.444.0

Profiling report - Test

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
14865480.390.060.010.017.413.833.5823.330.014.0
14865490.400.220.010.017.603.923.6813.400.014.0
14865500.410.050.010.017.794.023.7712.040.014.0
14865510.420.020.010.047.984.123.864.320.054.0
14865520.430.010.010.118.174.223.950.490.594.0
14865810.720.020.020.0313.687.066.626.280.044.0
14866211.120.930.060.0121.2810.9910.2940.250.014.0
14866221.130.740.060.0121.4711.0910.3834.520.014.0
14866231.140.690.060.0121.6611.1810.4831.310.014.0
14866241.150.690.060.0121.8511.2810.5730.080.014.0

Profiling report - Train

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
25169619.6934.6588.800.26184.1170.53113.58327.450.261.0
25169629.7035.8989.300.24184.3070.63113.67345.480.251.0
25169639.7139.0989.300.23184.4970.73113.76367.500.231.0
25169649.7241.4589.200.22184.6870.83113.85381.950.221.0
25169659.7340.3588.600.22184.8770.93113.94386.110.221.0
25169669.7440.4088.300.22185.0671.02114.04382.730.221.0
25169679.7540.4488.100.22185.2571.12114.13385.140.221.0
25169689.7641.1594.700.21185.4471.22114.22397.500.211.0
25169699.7744.1779.900.22185.6371.32114.31409.960.221.0
25169709.7844.37105.500.14185.8271.42114.40427.650.141.0

Profiling report - Test

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes
251161229.831.1927.902.34566.77292.63274.142.294.452.0
251161329.841.2027.902.33566.96292.73274.232.294.442.0
251161429.851.2027.802.32567.15292.83274.322.294.422.0
251161529.861.2027.902.32567.34292.93274.412.314.412.0
251161629.871.2027.802.32567.53293.02274.512.314.392.0
251161729.881.2027.802.32567.72293.12274.602.294.422.0
251161829.891.1927.702.32567.91293.22274.692.284.422.0
251161929.901.2027.702.32568.10293.32274.782.284.422.0
251162029.911.2027.702.31568.29293.42274.872.294.412.0
251162129.921.2027.502.29568.48293.52274.962.304.352.0

Duplicate rows

Profiling report - Train

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes# duplicates
Dataset does not contain duplicate rows.

Profiling report - Test

Depth (m)qc (MPa)fs (kPa)Rf (%)σ,v (kPa)u0 (kPa)σ',v (kPa)Qtn (-)Fr (%)Oberhollenzer_classes# duplicates
Dataset does not contain duplicate rows.